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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Predicting Body Mass Index From Structural MRI Brain Images Using a Deep Convolutional Neural Network
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, March 2020
|
| DOI | 10.3389/fninf.2020.00010 |
| Pubmed ID | |
| Authors |
Pál Vakli, Regina J. Deák-Meszlényi, Tibor Auer, Zoltán Vidnyánszky |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hong Kong | 1 | 13% |
| United States | 1 | 13% |
| Hungary | 1 | 13% |
| Russia | 1 | 13% |
| Unknown | 4 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 63% |
| Scientists | 2 | 25% |
| Science communicators (journalists, bloggers, editors) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 70 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 70 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 14 | 20% |
| Researcher | 9 | 13% |
| Student > Ph. D. Student | 9 | 13% |
| Student > Doctoral Student | 4 | 6% |
| Student > Bachelor | 3 | 4% |
| Other | 8 | 11% |
| Unknown | 23 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 19% |
| Medicine and Dentistry | 7 | 10% |
| Neuroscience | 7 | 10% |
| Engineering | 6 | 9% |
| Biochemistry, Genetics and Molecular Biology | 3 | 4% |
| Other | 9 | 13% |
| Unknown | 25 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 May 2021.
All research outputs
#6,834,712
of 25,622,179 outputs
Outputs from Frontiers in Neuroinformatics
#296
of 844 outputs
Outputs of similar age
#127,348
of 392,197 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#4
of 16 outputs
Altmetric has tracked 25,622,179 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 844 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 392,197 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.